Overview

Dataset statistics

Number of variables12
Number of observations641
Missing cells628
Missing cells (%)8.2%
Duplicate rows2
Duplicate rows (%)0.3%
Total size in memory60.2 KiB
Average record size in memory96.2 B

Variable types

Numeric10
Categorical2

Alerts

Dataset has 2 (0.3%) duplicate rowsDuplicates
p is highly overall correlated with cc and 1 other fieldsHigh correlation
t is highly overall correlated with sHigh correlation
ts is highly overall correlated with tdHigh correlation
td is highly overall correlated with tsHigh correlation
cc is highly overall correlated with pHigh correlation
pp is highly overall correlated with pHigh correlation
s is highly overall correlated with tHigh correlation
cc has 89 (13.9%) missing valuesMissing
pp has 526 (82.1%) missing valuesMissing

Reproduction

Analysis started2023-12-29 11:52:25.773157
Analysis finished2023-12-29 11:52:31.115757
Duration5.34 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

e
Real number (ℝ)

Distinct52
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.984399
Minimum18
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:31.164059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile22
Q131
median41
Q350
95-th percentile60
Maximum71
Range53
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.926588
Coefficient of variation (CV)0.29100312
Kurtosis-0.91294798
Mean40.984399
Median Absolute Deviation (MAD)10
Skewness0.042576109
Sum26271
Variance142.24351
MonotonicityNot monotonic
2023-12-29T05:52:31.221431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 25
 
3.9%
52 24
 
3.7%
47 24
 
3.7%
43 24
 
3.7%
38 23
 
3.6%
24 22
 
3.4%
51 21
 
3.3%
33 20
 
3.1%
41 19
 
3.0%
46 19
 
3.0%
Other values (42) 420
65.5%
ValueCountFrequency (%)
18 2
 
0.3%
19 3
 
0.5%
20 5
 
0.8%
21 8
 
1.2%
22 17
2.7%
23 10
1.6%
24 22
3.4%
25 16
2.5%
26 19
3.0%
27 15
2.3%
ValueCountFrequency (%)
71 1
 
0.2%
70 3
 
0.5%
69 1
 
0.2%
66 1
 
0.2%
65 3
 
0.5%
64 3
 
0.5%
63 2
 
0.3%
62 2
 
0.3%
61 9
1.4%
60 10
1.6%

s
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
1
442 
2
199 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters641
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 442
69.0%
2 199
31.0%

Length

2023-12-29T05:52:31.272454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-29T05:52:31.322028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 442
69.0%
2 199
31.0%

Most occurring characters

ValueCountFrequency (%)
1 442
69.0%
2 199
31.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 641
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 442
69.0%
2 199
31.0%

Most occurring scripts

ValueCountFrequency (%)
Common 641
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 442
69.0%
2 199
31.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 442
69.0%
2 199
31.0%

u
Categorical

Distinct4
Distinct (%)0.6%
Missing5
Missing (%)0.8%
Memory size5.1 KiB
1.0
402 
4.0
117 
3.0
59 
2.0
58 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1908
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 402
62.7%
4.0 117
 
18.3%
3.0 59
 
9.2%
2.0 58
 
9.0%
(Missing) 5
 
0.8%

Length

2023-12-29T05:52:31.357464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-29T05:52:31.403668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 402
63.2%
4.0 117
 
18.4%
3.0 59
 
9.3%
2.0 58
 
9.1%

Most occurring characters

ValueCountFrequency (%)
. 636
33.3%
0 636
33.3%
1 402
21.1%
4 117
 
6.1%
3 59
 
3.1%
2 58
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1272
66.7%
Other Punctuation 636
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 636
50.0%
1 402
31.6%
4 117
 
9.2%
3 59
 
4.6%
2 58
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 636
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 636
33.3%
0 636
33.3%
1 402
21.1%
4 117
 
6.1%
3 59
 
3.1%
2 58
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 636
33.3%
0 636
33.3%
1 402
21.1%
4 117
 
6.1%
3 59
 
3.1%
2 58
 
3.0%

p
Real number (ℝ)

Distinct310
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.332112
Minimum40.5
Maximum124.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:31.451569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum40.5
5-th percentile52
Q161.4
median69
Q378
95-th percentile96.25
Maximum124.5
Range84
Interquartile range (IQR)16.6

Descriptive statistics

Standard deviation13.180143
Coefficient of variation (CV)0.18739865
Kurtosis0.57590567
Mean70.332112
Median Absolute Deviation (MAD)8.45
Skewness0.69917326
Sum45082.884
Variance173.71617
MonotonicityNot monotonic
2023-12-29T05:52:31.501818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 14
 
2.2%
63 14
 
2.2%
73 13
 
2.0%
74 12
 
1.9%
69 11
 
1.7%
68 11
 
1.7%
64 11
 
1.7%
58 10
 
1.6%
71 10
 
1.6%
60 10
 
1.6%
Other values (300) 525
81.9%
ValueCountFrequency (%)
40.5 1
0.2%
41 1
0.2%
42 2
0.3%
43 1
0.2%
45.2 1
0.2%
46.7 1
0.2%
47.7 1
0.2%
47.95 1
0.2%
48 2
0.3%
48.1 1
0.2%
ValueCountFrequency (%)
124.5 1
0.2%
112.3 1
0.2%
110 1
0.2%
109.9 1
0.2%
109 1
0.2%
108 1
0.2%
107.8 1
0.2%
106.75 2
0.3%
105 1
0.2%
104 1
0.2%

t
Real number (ℝ)

Distinct45
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6015757
Minimum1.38
Maximum1.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:31.551760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.38
5-th percentile1.48
Q11.54
median1.59
Q31.66
95-th percentile1.75
Maximum1.91
Range0.53
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.085736775
Coefficient of variation (CV)0.053532766
Kurtosis0.12127825
Mean1.6015757
Median Absolute Deviation (MAD)0.06
Skewness0.52046523
Sum1026.61
Variance0.0073507947
MonotonicityNot monotonic
2023-12-29T05:52:31.604403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1.54 33
 
5.1%
1.6 33
 
5.1%
1.55 32
 
5.0%
1.56 32
 
5.0%
1.57 31
 
4.8%
1.52 30
 
4.7%
1.59 29
 
4.5%
1.58 25
 
3.9%
1.5 25
 
3.9%
1.62 25
 
3.9%
Other values (35) 346
54.0%
ValueCountFrequency (%)
1.38 1
 
0.2%
1.42 1
 
0.2%
1.43 2
 
0.3%
1.44 5
 
0.8%
1.45 7
 
1.1%
1.47 12
1.9%
1.48 11
1.7%
1.49 11
1.7%
1.5 25
3.9%
1.51 20
3.1%
ValueCountFrequency (%)
1.91 1
 
0.2%
1.89 2
 
0.3%
1.85 1
 
0.2%
1.83 3
0.5%
1.82 5
0.8%
1.81 1
 
0.2%
1.8 1
 
0.2%
1.79 4
0.6%
1.78 5
0.8%
1.77 3
0.5%

ts
Real number (ℝ)

Distinct81
Distinct (%)12.7%
Missing4
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean118.74882
Minimum80
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:31.657685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile96
Q1107
median118
Q3128
95-th percentile148
Maximum185
Range105
Interquartile range (IQR)21

Descriptive statistics

Standard deviation15.952258
Coefficient of variation (CV)0.13433614
Kurtosis0.4895311
Mean118.74882
Median Absolute Deviation (MAD)10
Skewness0.59537012
Sum75643
Variance254.47455
MonotonicityNot monotonic
2023-12-29T05:52:31.710988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 35
 
5.5%
100 27
 
4.2%
110 25
 
3.9%
121 22
 
3.4%
117 20
 
3.1%
123 19
 
3.0%
124 17
 
2.7%
111 16
 
2.5%
108 16
 
2.5%
105 16
 
2.5%
Other values (71) 424
66.1%
ValueCountFrequency (%)
80 1
 
0.2%
84 1
 
0.2%
85 1
 
0.2%
86 1
 
0.2%
87 3
0.5%
88 1
 
0.2%
89 1
 
0.2%
90 6
0.9%
91 1
 
0.2%
92 3
0.5%
ValueCountFrequency (%)
185 1
 
0.2%
170 1
 
0.2%
165 2
0.3%
164 1
 
0.2%
163 2
0.3%
162 2
0.3%
160 4
0.6%
159 1
 
0.2%
158 1
 
0.2%
155 1
 
0.2%

td
Real number (ℝ)

Distinct49
Distinct (%)7.7%
Missing4
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean75.356358
Minimum50
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:31.763339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q170
median75
Q381
95-th percentile89
Maximum115
Range65
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.8788232
Coefficient of variation (CV)0.11782447
Kurtosis0.3589601
Mean75.356358
Median Absolute Deviation (MAD)6
Skewness0.036133242
Sum48002
Variance78.833501
MonotonicityNot monotonic
2023-12-29T05:52:31.813655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
70 50
 
7.8%
80 37
 
5.8%
75 29
 
4.5%
81 28
 
4.4%
77 28
 
4.4%
73 27
 
4.2%
72 26
 
4.1%
60 24
 
3.7%
74 23
 
3.6%
69 22
 
3.4%
Other values (39) 343
53.5%
ValueCountFrequency (%)
50 2
 
0.3%
51 2
 
0.3%
52 1
 
0.2%
53 1
 
0.2%
54 1
 
0.2%
56 2
 
0.3%
57 2
 
0.3%
58 2
 
0.3%
59 3
 
0.5%
60 24
3.7%
ValueCountFrequency (%)
115 1
 
0.2%
103 1
 
0.2%
99 2
 
0.3%
98 1
 
0.2%
96 3
 
0.5%
94 3
 
0.5%
93 1
 
0.2%
92 2
 
0.3%
91 4
 
0.6%
90 13
2.0%

gs
Real number (ℝ)

Distinct81
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.377535
Minimum64
Maximum378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:31.868908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum64
5-th percentile76
Q184
median90
Q398
95-th percentile135
Maximum378
Range314
Interquartile range (IQR)14

Descriptive statistics

Standard deviation29.92085
Coefficient of variation (CV)0.31045461
Kurtosis30.051489
Mean96.377535
Median Absolute Deviation (MAD)7
Skewness4.8604598
Sum61778
Variance895.25724
MonotonicityNot monotonic
2023-12-29T05:52:31.923502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 33
 
5.1%
93 27
 
4.2%
86 27
 
4.2%
88 27
 
4.2%
87 26
 
4.1%
95 25
 
3.9%
89 23
 
3.6%
83 22
 
3.4%
90 21
 
3.3%
80 21
 
3.3%
Other values (71) 389
60.7%
ValueCountFrequency (%)
64 1
 
0.2%
66 2
 
0.3%
71 4
 
0.6%
72 6
0.9%
73 8
1.2%
74 4
 
0.6%
75 6
0.9%
76 14
2.2%
77 10
1.6%
78 9
1.4%
ValueCountFrequency (%)
378 1
0.2%
317 1
0.2%
303 2
0.3%
262 1
0.2%
256 1
0.2%
255 2
0.3%
243 1
0.2%
236 1
0.2%
216 1
0.2%
208 1
0.2%

c
Real number (ℝ)

Distinct150
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186.69111
Minimum85
Maximum356
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:31.979632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum85
5-th percentile133
Q1159
median182
Q3211
95-th percentile247
Maximum356
Range271
Interquartile range (IQR)52

Descriptive statistics

Standard deviation38.343897
Coefficient of variation (CV)0.20538684
Kurtosis1.061402
Mean186.69111
Median Absolute Deviation (MAD)25
Skewness0.58430747
Sum119669
Variance1470.2544
MonotonicityNot monotonic
2023-12-29T05:52:32.037170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
175 13
 
2.0%
204 12
 
1.9%
173 11
 
1.7%
176 11
 
1.7%
167 11
 
1.7%
161 11
 
1.7%
172 10
 
1.6%
158 10
 
1.6%
187 10
 
1.6%
242 10
 
1.6%
Other values (140) 532
83.0%
ValueCountFrequency (%)
85 1
 
0.2%
94 2
0.3%
97 1
 
0.2%
99 3
0.5%
108 1
 
0.2%
109 1
 
0.2%
110 1
 
0.2%
115 1
 
0.2%
117 2
0.3%
119 2
0.3%
ValueCountFrequency (%)
356 1
0.2%
347 1
0.2%
341 1
0.2%
316 1
0.2%
302 1
0.2%
301 1
0.2%
295 1
0.2%
290 1
0.2%
282 1
0.2%
281 1
0.2%

hdl
Real number (ℝ)

Distinct57
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.872075
Minimum21
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:32.092360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile29
Q137
median43
Q350
95-th percentile63
Maximum96
Range75
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.635841
Coefficient of variation (CV)0.24242849
Kurtosis2.243345
Mean43.872075
Median Absolute Deviation (MAD)7
Skewness1.0082697
Sum28122
Variance113.12111
MonotonicityNot monotonic
2023-12-29T05:52:32.146737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 35
 
5.5%
42 32
 
5.0%
40 31
 
4.8%
39 29
 
4.5%
46 28
 
4.4%
45 26
 
4.1%
41 23
 
3.6%
33 22
 
3.4%
34 21
 
3.3%
50 21
 
3.3%
Other values (47) 373
58.2%
ValueCountFrequency (%)
21 1
 
0.2%
24 3
 
0.5%
25 3
 
0.5%
26 1
 
0.2%
27 10
1.6%
28 12
1.9%
29 6
 
0.9%
30 11
1.7%
31 19
3.0%
32 15
2.3%
ValueCountFrequency (%)
96 1
0.2%
93 2
0.3%
84 1
0.2%
79 1
0.2%
78 1
0.2%
77 1
0.2%
76 1
0.2%
74 1
0.2%
72 2
0.3%
70 2
0.3%

cc
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct128
Distinct (%)23.2%
Missing89
Missing (%)13.9%
Infinite0
Infinite (%)0.0%
Mean89.47587
Minimum52
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:32.205175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile70
Q182
median89
Q397
95-th percentile109
Maximum123
Range71
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.893636
Coefficient of variation (CV)0.13292562
Kurtosis-0.06237426
Mean89.47587
Median Absolute Deviation (MAD)8
Skewness0.030935481
Sum49390.68
Variance141.45857
MonotonicityNot monotonic
2023-12-29T05:52:32.257773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 24
 
3.7%
93 19
 
3.0%
90 19
 
3.0%
102 19
 
3.0%
84 18
 
2.8%
85 18
 
2.8%
89 17
 
2.7%
82 16
 
2.5%
96 14
 
2.2%
76 14
 
2.2%
Other values (118) 374
58.3%
(Missing) 89
 
13.9%
ValueCountFrequency (%)
52 1
 
0.2%
53 1
 
0.2%
55 1
 
0.2%
57 1
 
0.2%
60 1
 
0.2%
65 1
 
0.2%
66 4
0.6%
67 3
 
0.5%
68 2
 
0.3%
69 9
1.4%
ValueCountFrequency (%)
123 1
 
0.2%
121 1
 
0.2%
119 2
0.3%
118 1
 
0.2%
117 4
0.6%
116 1
 
0.2%
115 4
0.6%
114 1
 
0.2%
113 1
 
0.2%
112 1
 
0.2%

pp
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)20.0%
Missing526
Missing (%)82.1%
Infinite0
Infinite (%)0.0%
Mean34.797826
Minimum19
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-29T05:52:32.305781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile30
Q132
median35
Q337
95-th percentile40
Maximum42
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6710364
Coefficient of variation (CV)0.10549614
Kurtosis2.2192141
Mean34.797826
Median Absolute Deviation (MAD)2.4
Skewness-0.66642513
Sum4001.75
Variance13.476508
MonotonicityNot monotonic
2023-12-29T05:52:32.350505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
35 14
 
2.2%
32 14
 
2.2%
36 11
 
1.7%
37 10
 
1.6%
31 8
 
1.2%
39 8
 
1.2%
40 8
 
1.2%
30 7
 
1.1%
34 7
 
1.1%
33 7
 
1.1%
Other values (13) 21
 
3.3%
(Missing) 526
82.1%
ValueCountFrequency (%)
19 1
 
0.2%
24 1
 
0.2%
27.5 1
 
0.2%
30 7
1.1%
31 8
1.2%
32 14
2.2%
32.9 2
 
0.3%
33 7
1.1%
33.6 2
 
0.3%
34 7
1.1%
ValueCountFrequency (%)
42 2
 
0.3%
41.15 1
 
0.2%
41 2
 
0.3%
40 8
1.2%
39 8
1.2%
38 4
 
0.6%
37.4 1
 
0.2%
37 10
1.6%
36 11
1.7%
35.9 1
 
0.2%

Interactions

2023-12-29T05:52:30.406849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:26.155968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:26.758074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.201029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.645030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.081176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.638975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.082013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.510696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.954631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:30.444145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:26.224804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:26.801735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.245196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.687702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.126343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.684935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.124358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.554143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.998325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:30.484344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:26.282800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:26.844839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.285542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.727713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.169692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.726813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.164902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.598595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:30.041357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:30.645313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:26.345266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:26.889297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.329842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.771090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.215538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.773068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.208901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.642741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:30.086657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:30.687480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:26.493001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:26.934228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.374715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.814388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.260405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.817017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.250439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.687523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:30.132033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:30.722718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:26.537469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:26.980136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.421316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.859441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.414034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.863589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.294006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.734505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:30.177961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:30.758646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:26.583790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.024335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.466832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.905237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.457954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.908697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.339847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.780374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:30.223854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:30.798623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:26.627538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.067818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.511262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.949827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.504141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.952453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.381543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.823629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:30.271648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:30.833406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:26.674363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.112638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.558999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.996987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.554421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.999705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.427581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.872136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:30.319580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:30.875765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:26.720776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.162887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:27.607160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.042858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:28.602111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.045845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.471844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:29.917962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-29T05:52:30.367528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-12-29T05:52:32.394518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
epttstdgschdlccppsu
e1.0000.060-0.2050.2820.1450.3280.3070.1160.203-0.2680.0990.000
p0.0601.0000.4530.3820.3390.2580.052-0.2970.8080.5450.3040.096
t-0.2050.4531.0000.0840.082-0.017-0.079-0.1510.1520.1270.6570.070
ts0.2820.3820.0841.0000.7660.2690.118-0.0960.3540.2340.1200.069
td0.1450.3390.0820.7661.0000.1510.128-0.1130.3040.1570.0000.000
gs0.3280.258-0.0170.2690.1511.0000.064-0.1740.2710.0220.0860.000
c0.3070.052-0.0790.1180.1280.0641.0000.2950.1320.0170.1180.066
hdl0.116-0.297-0.151-0.096-0.113-0.1740.2951.000-0.221-0.2810.2170.000
cc0.2030.8080.1520.3540.3040.2710.132-0.2211.0000.4200.1320.092
pp-0.2680.5450.1270.2340.1570.0220.017-0.2810.4201.0000.2850.137
s0.0990.3040.6570.1200.0000.0860.1180.2170.1320.2851.0000.108
u0.0000.0960.0700.0690.0000.0000.0660.0000.0920.1370.1081.000

Missing values

2023-12-29T05:52:30.934003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-29T05:52:31.014064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-29T05:52:31.082743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

esupttstdgschdlccpp
01811.062.01.55101.070.0851885381.0NaN
11821.070.01.6793.063.0861555490.0NaN
21911.056.01.58109.073.0931436067.033.6
31911.052.21.59108.075.0931436067.033.6
41911.053.01.54118.072.0831465273.0NaN
52021.062.01.6990.060.0641633772.0NaN
62021.061.81.78106.072.0991463574.031.0
72021.060.51.78109.062.0991463575.031.0
82021.059.01.71117.074.0841944678.2NaN
92111.048.11.5799.064.0921673972.5NaN
esupttstdgschdlccpp
6316414.074.0001.45120.070.015730249111.00NaN
6326414.060.0001.49100.070.09023264NaNNaN
6336914.077.0001.52110.050.013813725110.0030.0
6347024.062.7001.53120.064.02081705357.0033.0
6357014.094.0001.51120.060.01761633276.0036.0
636352NaN78.0001.69129.086.0792213698.20NaN
637361NaN99.0001.68125.088.010018950117.0037.4
638412NaN71.8751.64100.064.0812043388.7539.0
639422NaN63.3001.69121.075.0802304883.00NaN
640441NaN93.6001.52120.071.010417546108.00NaN

Duplicate rows

Most frequently occurring

esupttstdgschdlccpp# duplicates
02221.062.31.71106.076.0761493481.0NaN2
15813.078.41.50160.080.09521143109.0NaN2